Understanding Potential Dietary Drivers of Longevity Across Countries
Introduction
Diet plays a vital role in influencing health and longevity. A balanced intake of macronutrients—such as carbohydrates, fats, and proteins—has been linked to increased life expectancy. However, the relationship between nutrition and longevity is complex, influenced by factors like healthcare, socio-economic conditions, and lifestyle.
This analysis explores whether macronutrient intake, particularly caloric consumption and the balance of animal- versus plant-based proteins, correlates with life expectancy across countries. The research questions driving this analysis include:
1. How does life expectancy and caloric intake compare geographically across countries?
2. How do different macronutrient ratios influence life expectancy?
3. What is the impact of a plant- versus animal-based diet on life expectancy?
The objective is to understand how the consumption of certain macronutrients affects life expectancy, focusing on three country groups ranked by life expectancy (top 10, middle 10, and bottom 10 countries). This analysis seeks to uncover patterns that explain the relationship between diet and longevity, providing insights relevant for policymakers, healthcare professionals, and nutritionists.
Contrary to some studies advocating caloric restriction, we found that countries with higher life expectancy often consume more calories. This suggests that calorie quality and access to healthcare may be more influential than calorie quantity. Additionally, countries with higher life expectancy consumed more animal-based proteins, which aligns with the theory that animal proteins support better overall health. Meanwhile, life expectancy in some lower-income countries continues to rise despite lower caloric intake, highlighting the importance of potential factors like healthcare access.
This report shows potential limitations that are severalfold. While diet is a key factor, other elements like healthcare, food accessibility, and socio-economic conditions also affect longevity. The quality and availability of data may also vary between countries, potentially influencing the results.
Theoretical Focus
Understanding the nutritional drivers of life expectancy requires examining the influence of nutrition and dietary patterns. Research highlights the importance of health-related behaviours—such as maintaining a healthy BMI, regular physical activity, and a nutritious diet—in extending life expectancy. For instance, Li et al. (2018) found that adopting these factors could extend life by up to 14 years for women and 12 years for men compared to those with higher-risk behaviours.
Diet plays a crucial role in longevity. Specific dietary habits, such as caloric and methionine restriction, are linked to longer lifespans and reduced mortality (Ekmekcioglu, 2020). The consumption of whole grains, vegetables, fruits, nuts, and coffee has consistently been associated with increased lifespan and improved health outcomes.
Macronutrient Balance and Global Variability
Macronutrient intake, which includes proteins, carbohydrates, and fats, plays a significant role in public health. While protein intake remains relatively stable globally, carbohydrate and fat consumption vary more widely across regions, reflecting economic and cultural differences (Lieberman et al., 2020). The WHO recommends limiting fat to 30% of total energy intake, prioritizing whole grains, vegetables, and fruits in diets.
Animal vs. Plant-Based Protein
There is an ongoing debate about the health impacts of animal- versus plant-based proteins. Research shows that animal proteins support better muscle synthesis due to their amino acid content (van Vliet et al., 2015), though plant-based diets may reduce chronic disease risks and have lower environmental impacts (Godfray et al., 2018). Economic factors influence these dietary choices, with lower-income populations relying more on plant proteins, while wealthier countries tend to consume more animal products.
Dietary Uncertainty
Despite extensive research, no consensus exists on the “best” diet for health. Katz and Meller (2014) argue that minimally processed, plant-based diets are most strongly linked to health promotion. However, macronutrient intake varies globally, making universal dietary recommendations challenging.
Methodology
Data Sources
For this analysis, we utilized three different datasets, which were merged and cleaned to provide insights into life expectancy, nutritional intake, and the geographical context of each country. The datasets are as follows:
Nutritional Intake Data
This dataset is from the Food and Agriculture Organization (FAO) of the United Nations and contains information on a country’s food supply. It provides data on various food items—covering both primary and processed commodities that are potentially available for human consumption—and details their sources and utilization.
The dataset contains 32,910 rows and 15 columns and is formatted in long format. It spans the period from 2010 to 2021. The key variables used for our analysis are:
- Country: The country from which the data is collected.
- Element: The macronutrient value in grams or kilocalories per capita per day.
- Item: The type of food item, specifying whether it is a vegetable, meat, or other source.
- Year of Consumption: The year for which the data is recorded.
Life Expectancy Data
The dataset titled “Life Expectancy from 2000 to 2021” from the World health Organisation provides data on life expectancy trends across countries. It contains 24420 rows and 34 columns and spans the years from 2000 to 2021. The data is structured in a long format.
Key variables that we used from this dataset:
- Country: The country for which life expectancy data is recorded.
- Year: The year corresponding to the life expectancy data.
- Life Expectancy: The average life expectancy at birth for each country in years.
Geographical Data:
The third dataset includes the geographical coordinates of the capital cities of the countries, which was used to map and visualize the location of each country in relation to the life expectancy and nutritional intake data.
Data Preparation
To ensure the datasets were ready for analysis, several preprocessing steps were taken:
Data Cleaning
Handling Missing Values: We identified and addressed missing values in the datasets. Fortunately, there were no missing values in the critical columns that were necessary for our analysis.
Dropping Unnecessary Variables: Any variables not relevant to our analysis were removed to streamline the data and focus on the essential components.
Data Transformation
The data was transformed to ensure consistency across all datasets. For example, variables such as nutritional intake were normalized to ensure comparability between countries with varying population sizes.
Data Merging:
The three datasets were merged using the country variable as the key. This allowed for the integration of life expectancy, nutritional intake, and geographic information into a single dataset.
Data Manipulation
For our analysis, we categorized countries into three groups based on life expectancy:
- Top 10 countries with the highest life expectancy.
- Middle 10 countries with life expectancy in the middle range.
- Bottom 10 countries with the lowest life expectancy.
Countries of Focus
Top 10
| Country | Capital_City | Life_Expectancy | Caloric_Intake |
|---|---|---|---|
| Japan | Tokyo | 87.22 | 2675 |
| Republic of Korea | Seoul | 86.76 | 3413 |
| Spain | Madrid | 85.37 | 3401 |
| Switzerland | Bern | 85.21 | 3414 |
| Australia | Canberra | 84.98 | 3479 |
| France | Paris | 84.76 | 3577 |
| Sweden | Stockholm | 84.67 | 3287 |
| Italy | Roma (Rome) | 84.39 | 3720 |
| Norway | Oslo | 84.35 | 3511 |
| Cyprus | Lefkosia (Nicosia) | 84.23 | 3036 |
Middle 10
| Country | Capital_City | Life_Expectancy | Caloric_Intake |
|---|---|---|---|
| Serbia | Beograd (Belgrade) | 75.73 | 3576 |
| Azerbaijan | Baku | 75.63 | 3356 |
| Malaysia | Kuala Lumpur | 75.61 | 2980 |
| Bahrain | Al-Manamah (Manama) | 75.43 | 3480 |
| Saint Lucia | Castries | 75.39 | 2717 |
| Peru | Lima | 75.31 | 2801 |
| Oman | Masqat (Muscat) | 75.27 | 3032 |
| Bangladesh | Dhaka | 75.22 | 2567 |
| Bulgaria | Sofia | 75.15 | 2980 |
| Mexico | Ciudad de México (Mexico City) | 74.89 | 3292 |
Bottom 10
| Country | Capital_City | Life_Expectancy | Caloric_Intake |
|---|---|---|---|
| Niger | Niamey | 62.19 | 2507 |
| Guinea-Bissau | Bissau | 62.08 | 2292 |
| South Sudan | Juba | 61.91 | 2414 |
| Afghanistan | Kabul | 61.73 | 2240 |
| Chad | N’Djaména | 61.70 | 2191 |
| Zimbabwe | Harare | 61.52 | 2058 |
| Mozambique | Maputo | 61.51 | 2169 |
| Somalia | Muqdisho (Mogadishu) | 57.47 | 1834 |
| Central African Republic | Bangui | 56.58 | 2246 |
| Lesotho | Maseru | 55.99 | 1800 |
Within these groups, we focused on analyzing the highest caloric intake for each year available in the dataset. Additionally, for life expectancy, we selected the most recent and highest value for each country to ensure that our analysis reflected the best available data for each country over the period of study.
Data Analysis
We analyzed the nutritional intake for each country group over time, focusing on key macronutrients such as fat, protein, and carbohydrates. This helped identify trends in dietary intake that may correlate with life expectancy. Further analysis included more specific macronutrients, such as Animal vs. vegetal intake, Protein sources.
Visualizations were created using mostly the ggplot2 package in R, while ensuring that all plots were color-blind safe. Whenever possible, interactive visualizations were produced to enhance user interaction and provide additional insights.
As part of our analysis, we ran a simple linear regression model to analyze the trend in global life expectancy over time. In this model, life expectancy was used as the dependent variable, while the year was set as the independent variable. This approach allowed us to examine whether there was a consistent increase in life expectancy from 2010 to 2021.
Limitations of our approach
With these datasets come several limitations that need to be acknowledged in our analysis. First, it is important to recognize that the nutritional intake data for every individual in a country cannot be perfectly captured. As a result, the quality and accuracy of the data may vary across countries, and data collection in certain regions may be more challenging due to accessibility issues.
Furthermore, there is some uncertainty regarding how the data was collected. It is unclear whether macronutrient intakes were self-reported by individuals or gathered by the respective organizations through other means, which could affect the reliability of the data.
Within countries, there may also be significant regional differences in nutritional intake and life expectancy, which are not accounted for in this analysis. By aggregating countries into top, middle, and bottom categories, we risk oversimplifying complex factors. This approach might mask important nuances in dietary habits and life expectancy trends that are specific to individual regions or countries.
Additionally, while this report primarily focuses on the role of nutrition, it is essential to note that non-dietary factors—such as healthcare access, education, and socio-economic conditions—also play a significant role in influencing life expectancy (as discussed in the Theoretical Focus section).
Use of Generative AI
We used ChatGPT as our Generative AI tool during our work on R tasks, which presented both opportunities and challenges. One challenge we faced was handling data effectively, especially when we wanted to use specific methods introduced during the course, such as data wrangling with dplyr or visualizations with ggplot2. While it was up to us to decide how to approach the tasks, we preferred to follow these methods to maintain consistency with what we had learned. However, when we asked ChatGPT for assistance, it sometimes suggested alternative approaches using different packages or methods, which did not always align with our chosen way of working. This required us to manually adjust the AI’s suggestions to match our goals.
An important aspect of using ChatGPT was the need to clearly and explicitly state our objectives. For example, when we asked for help with data preprocessing, ChatGPT often didn’t fully grasp the context or the specific transformations we needed. As a result, it occasionally generated solutions that weren’t quite suited to our dataset or analysis. We had to carefully communicate exactly what we wanted to achieve and refine the questions we posed to get more relevant output.
That said, ChatGPT proved useful for simpler tasks, such as generating code for basic data cleaning or debugging common errors. However, we consistently had to double-check the output to ensure it met our needs, especially when the AI’s suggestions diverged from the methods we wanted to use. This experience highlighted the importance of clear communication and human oversight when integrating AI into the workflow, ensuring that the results were aligned with our project’s specific goals and methods.
Results and Discussion
In the following discussion, we will explore the key findings from the analysis and compare them with existing literature on nutrition and longevity. We will examine how caloric intake, macronutrient consumption, and the balance of plant- and animal-based proteins influence life expectancy across different countries. Additionally, we will discuss the disparities observed between high and low life expectancy countries.
RQ1: How does life expectancy and caloric intake compare geographically across countries?
Geographical Patterns of Life Expectancy and Nutritional Intake [Chapter of Choice]
Plot 1: Countries by Mean Life Expectancy
Plot 2: World Map of Categorized Countries
Plot 1 illustrates life expectancy for countries in the top 10, middle 10, and bottom 10 categories. The accompanying map (Plot 2) geographically locates countries from these three groups. Hovering over the dots on the map or the bars in the chart reveals details for each country, such as the country name, its average life expectancy, and the average caloric intake.
From the bar chart, we observe that Japan leads in life expectancy, followed closely by South Korea, Spain, and France. Noticeably, the gap in life expectancy between the countries in the top and middle categories is relatively small. There is a more significant decline in life expectancy when we compare the life expectancy of countries from the middle group with the bottom group. The countries in the bottom category are predominantly located in Africa, particularly near the equator or in southern regions. In contrast, the middle group of countries includes nations from Asia, Africa, and Latin America, reflecting a more geographically diverse mix.
The countries from the top category are more dispersed, covering Europe, the Middle East, East Asia, and Oceania. Upon first inspection, it becomes evident that the countries in the top category are primarily from the developed world, while the middle category includes less developed countries, and the bottom category comprises the least developed nations.
Trends in Life Expectancy Over the Last Decade
Plot 3: Change in Life Expectancy 2010-2021
To get a closer look at the developments of life expectancy between 2010 and 2021, let’s consider plot 3. Each line in the chart represents one country, and hovering over it displays details such as the country name and specific data points. The plot shows that countries from the top and middle categories have relatively similar life expectancy, while there is a bigger gap to the countries in the bottom category.
Notably, life expectancy has generally risen over the last decade for countries in all three categories. However, a sharp drop is visible in 2020, which studies suggest may be attributed to the COVID-19 pandemic. According to Cao et al. (2023), global life expectancy declined by an average of 1.8 years due to the pandemic, with Latin America being the most affected region.
The impact of the pandemic is more pronounced in middle-category countries, where the drop in life expectancy was larger than in the top-category countries. This may be due to the varying levels of access to healthcare during the pandemic, though this is speculative and requires further investigation. In contrast, the countries in the top category countries experienced a less significant decline, perhaps because of better access to healthcare.
The gap between the middle and bottom countries is relatively large. While both groups have seen improvements in life expectancy over the years, the bottom-category countries remain far behind. Only three countries of the bottom group —Somalia, Lesotho, and the Central African Republic—experienced a notable decline during the pandemic.
This plot shows that life expectancy has risen globally over the past decade, the effects of external factors such as the COVID-19 pandemic have caused noticeable disruptions, particularly in less developed nations. These trends highlight the importance of addressing healthcare inequities, especially in the wake of global crises.
Global Trend in Life Expectancy Over Time
Linear Model Representation
Model Summary
##
## Call:
## lm(formula = model_df_clean$Life.Expectancy ~ model_df_clean$Year)
##
## Residuals:
## Min 1Q Median 3Q Max
## -21.117 -5.387 1.962 5.772 11.766
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -149.98078 24.92179 -6.018 1.79e-09 ***
## model_df_clean$Year 0.11138 0.01236 9.008 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.362 on 29728 degrees of freedom
## Multiple R-squared: 0.002722, Adjusted R-squared: 0.002689
## F-statistic: 81.15 on 1 and 29728 DF, p-value: < 2.2e-16
In this plot we can see that life expectancy on a global level has been more or less consistently rising in the past years. Although the COVID-19 pandemic did affect life expectancy in most countries, we treated those values as outliers, as this data cannot be concluded to be a consistent factor in longevity on a worldwide level.
The summary of the model shows us that the rate of increase is about
0.11138 years of life expectancy every year that passes, if
analyzed as a linear trend, with a non-insignificant p-value. This means
that, on average, every year that goes by, humanity is expected to live
around one month and 10 days longer than they were previously.
Overall Caloric Intake and its Relationship with Life Expectancy
Plot 4: Change in Caloric Intake 2010-2021
Next, when considering caloric intake, we see that countries in the top 10 life expectancy group also have the highest caloric intake, followed by the middle 10 countries, with the bottom 10 countries ingesting the least (plot 4). From this, a possible initial relationship emerges: countries with higher life expectancy also tend to consume more calories.
However, when examined more closely, it becomes evident that the caloric intake for the top and middle countries is rising steadily, but at different rates. The middle countries show a sharper increase in caloric intake, which may reflect their improving economic and healthcare conditions. In contrast, the bottom 10 countries show a slight decline in caloric intake over the years, even as their life expectancy rises. This counterintuitive trend suggests that while caloric intake plays a role in life expectancy, other significant factors must also be at play.
From these initial observations, we can condense three main points:
Countries in the top 10 life expectancy group have had a relatively stable life expectancy since 2010, with only a slight drop during the COVID-19 pandemic. These countries also consume the most calories across the observed period.
The middle group, while still developing, shows progress: both life expectancy and caloric intake have risen since 2010. Their caloric intake is the second highest among the three groups, and their life expectancy is gradually approaching the levels seen in the top category.
The bottom 10 countries also show an increase in life expectancy over the years, but their caloric intake has declined slightly. Despite consuming fewer calories, these countries have managed to improve their life expectancy, indicating that factors beyond nutrition—such as healthcare access and disease prevention—could be contributing to these gains.
RQ2: How do different macronutrient ratios influence life expectancy?
The Influence of Macronutrient Consumption on Life Expectancy
Plot 5: Nutritional Profile Comparison
With this initial observation, we can consider the second research question and ask, if the consumption of different macronutrients might impact life expectancy. Plot 5 illustrates the nutritional profile for each life expectancy category, displaying the average intake of fat, protein, and total caloric intake per day from 2010 to 2021.
As expected, countries with the highest life expectancy also have the highest overall caloric intake, followed by the middle category, with the bottom category lagging behind. The top and middle countries have shown a steady increase in fat consumption over the years. Also, in the top and middle groups, caloric intake is either gradually increasing or remaining relatively stable. However, the bottom category shows a decline in overall caloric intake, while fat and protein intake – important nutritional components - remain more or less constant.
At this stage, it becomes difficult to draw a direct relationship between life expectancy and caloric intake. While the data for the top and middle categories might suggest that higher food intake leads to higher life expectancy, the bottom group raises questions about the impact of other factors. For the bottom category, where food intake (particularly total calories) has decreased, life expectancy continues to rise (see plot 3). This leads to speculation about whether factors such as healthcare, hygiene, or other social determinants are playing a more significant role than nutrition alone.
Caloric Intake and the Complexities of Life Expectancy
Plot 6: Average intake across different countries
While the previous plots might lead to the assumption that higher caloric intake is tied to a higher life expectancy, Plot 6 demonstrates that this is not necessarily the case. This plot provides a detailed view of all the countries under focus, showing their average caloric intake, broken down into fat, protein, and total calories.
In this plot, the more saturated bars represent the countries from the top 10 life expectancy group, while the lighter bars correspond to countries in the middle and bottom groups. Countries from the top 10 are more concentrated on the left side of the plot, confirming that, on average, they tend to consume more calories. However, an interesting anomaly is Japan, the country with the highest life expectancy, which ranks only 17th out of the 30 countries in terms of daily caloric intake.
Similarly, Lesotho, which has the lowest life expectancy, does not have the lowest caloric intake, further suggesting that there are other contributing factors beyond daily caloric consumption that influence life expectancy. This emphasizes the need to consider other variables such as healthcare quality, access to clean water, and disease prevalence when analyzing the relationship between nutrition and longevity.
Thus, while caloric intake appears to play some role in life expectancy, this plot highlights the complexities of nutrition and its varied effects across countries, where high caloric intake does not always correspond to a longer life.
RQ3: What is the impact of a plant- versus animal-based diet on life expectancy?
Animal and Vegetal-Based Product Consumption Across Life Expectancy Categories
Plot 7: Average Daily Calories Consumed from Animal vs Vegetal Products
Plot 7 provides a closer look of the proportion of calories consumed from animal and plant-based products across the three life expectancy categories. The results in the plot align with previous observations that countries in the top life expectancy category consume the most calories, but reveals that the highest proportion of those calories come from animal products.
When looking at the top category, it is noticeable that the intake of both animal and plant-based products has remained relatively stable over time, although there has been a slight rise in animal product consumption. The middle category, however, shows a more significant increase in the consumption of both animal and plant-based products over recent years. This increase might be tied to economic growth and rising wealth among the populations in these countries. As animal products tend to be more expensive, it is probable that as incomes rise, people can afford to incorporate more of these foods into their diets. Another factor might be the increased accessibility of these products due to globalization, which has made food sources from across the world more available to these countries.
The bottom category presents a contrasting pattern, with a slight drop in plant-based product consumption and stable consumption of animal products over the years. This stability in animal product consumption, despite the drop in overall caloric intake, could suggest that while access to food has diminished in quantity, the quality or type of available food has remained constant.
The lack of significant growth in their consumption patterns could highlight the disparity between richer and poorer nations. Although we cannot see individual countries in this plot, the general trend suggests that these countries might be struggling with the economic and infrastructure challenges that restrict their access to diverse food sources.
Animal vs. Vegetal Product Ratios and Regional Dietary Patterns
Plot 8: Ratio of Animal vs. Vegetal Products
Plot 8 provides a more detailed breakdown of the ratio between animal and vegetal products consumed in each country. A lower ratio indicates that the consumption of animal and vegetal products is relatively equal, while a higher ratio suggests a greater difference between the two, with either animal or vegetal products dominating the diet.
The countries in the top category show the lowest and most stable ratios, indicating that their diets are relatively balanced in terms of animal and vegetal product consumption. This balance has been maintained consistently over the years across all top-category countries. In the mid-category, the ratio has gradually dropped over time, indicating a trend toward a more balanced consumption of animal and plant products. Bulgaria stands out as an outlier in the middle category, where a significant drop in the ratio occurred between 2020 and 2021.
The bottom category reveals the most intriguing developments in the ratio of animal to vegetal product consumption. The spread of ratios is the greatest here, with some countries, like Somalia, exhibiting low ratios, suggesting their diet is more similar to those of the top 10 countries. On the other hand, Mozambique shows a much higher ratio, indicating a diet more heavily reliant on plant-based foods compared to animal products.
These differences in the ratios across countries may reflect various regional dietary patterns. While it might be expected that different regions have varied access to or preferences for animal and plant products, this is not the case across all categories. For example, countries from both the top and middle categories are geographically diverse (e.g. Switzerland in Europa and Japan in Asia), yet their ratios remain relatively stable. In contrast, all the bottom-category countries are in Africa, yet their ratios differ widely, suggesting that factors beyond region, such as economic access or cultural preferences, might influence these dietary patterns.
Additionally, the development of these ratios over time is more volatile and less linear in the bottom category, hinting that these countries may experience shifts in their diets more frequently, possibly due to changing economic conditions or varying food availability.
These findings underscore the complexity of food consumption patterns across different countries and suggest that changing eating habits, whether due to economic shifts, access to resources, or cultural influences, play a significant role in shaping these ratios.
Protein Consumption Patterns: Animal vs. Plant Sources
Plot 9: Animal vs. Plant Protein
Plot 9 takes a deeper look at protein consumption by breaking down the sources—whether animal or vegetal. It highlights the part of protein sources consumed across the top, middle, and bottom life expectancy categories. When observing the blue bars, representing the average protein intake from vegetal sources, we see a similar pattern across all categories, with the highest bars for the middle category, followed by the top category, and finally the bottom category, which consumes the least amount of protein from vegetal sources.
However, the most visible difference is in the intake of animal protein. The top countries consume significantly more protein from animal sources, with an average intake between 65 and 68 grams, and this trend is on the rise. The middle-category countries, while still consuming significant vegetal protein, have reached a point where animal protein intake has increased and even surpassed plant-based protein consumption. The bottom-category countries, on the other hand, show the lowest intake of animal protein, consuming only between 15 and 20 grams on average.
These trends lead to important questions. As seen in other plots, the middle-category countries seem to shift toward consuming more animal protein as they develop economically. It aligns with the idea that as countries get wealthier, they can afford more animal products. Conversely, the bottom-category countries simply might not be able to afford animal protein, as it is too expensive for them, which may contribute to their reliance on plant-based protein.
Variations in Animal vs. Plant Protein Ratios Across Countries
Plot 10: Ratio of Animal vs. Plant Based
Similar to Plot 8, Plot 10 illustrates the ratio of animal to plant-based protein intake across the top, middle, and bottom country categories. Interestingly, across all countries, the ratios are relatively volatile, with noticeable variation over the years.
Starting with the top category, we observe that countries like Australia, Sweden, Switzerland, France, and Spain have the highest ratios, meaning that they derive most of their protein from animal sources. However, there seems to be a slight downward trend in the consumption of animal protein in these countries, indicating a shift towards more balanced choice of protein sources, or perhaps a reduction in animal product consumption altogether.
Within the top category, there is a gap between these countries and others, such as Italy, Norway, Korea, Cyprus, and Japan, which tend to consume less animal protein, though their consumption is on the rise.
In the middle category, there is no clear clustering or discernible pattern. The consumption of animal protein appears versatile and fluctuates over the years. Countries like Saint Lucia, for instance, consume a relatively high portion of animal protein, while Bangladesh, at the lower end, consumes as little animal protein as some of the bottom-category countries.
The bottom category displays a wide range of consumption patterns as well. Some countries, like Zimbabwe, consume very low amounts of animal protein, though their intake is slowly rising. Interestingly, some bottom-category countries consume nearly as much animal protein as top-category countries. For instance, the consumption in Zimbabwe is approaching that of the top group, while others, like Niger, remain consistently low in their animal protein consumption.
The variation in animal vs. plant protein consumption across these countries reflects not only economic differences but also shifts in dietary choices and access to different types of protein. The top countries may be choosing to reduce their animal protein intake, while bottom-category countries are still striving to afford more animal products, resulting in an interesting overlap in consumption patterns.
Comparison with Existing Literature on Caloric Intake and Longevity
When comparing these findings with existing literature, some patterns align while others differ. Studies like Ekmekcioglu (2020) suggest that longevity is linked to dietary practices such as caloric restriction and nutrient-dense foods. However, the results here show that countries with the highest life expectancy also tend to consume the most calories, which contrasts with the literature advocating lower caloric intake for longevity.
This suggests factors beyond caloric intake, such as healthcare access and diet quality, may play a key role. For instance, Japan, despite having the highest life expectancy, ranks lower in caloric intake, indicating that a balanced diet and lifestyle may be more important than calorie count alone. Additionally, life expectancy continues to rise in some lower-income countries despite decreasing caloric intake, further challenging the direct link between calories and longevity.
Conclusion
This report explored the relationship between life expectancy, nutrition, and dietary patterns across various countries, revealing several important insights. While countries with the highest life expectancy generally consume more calories, this finding does not fully align with the literature, which often promotes caloric restriction for longevity. Additionally, the balance between animal and plant-based protein consumption highlights the complex factors influencing life expectancy.
Higher life expectancy countries exhibit not only greater caloric intake but also a more balanced diet, with economic and cultural factors playing a significant role. In contrast, some lower-income countries have managed to improve life expectancy despite lower caloric intake, suggesting that healthcare access, diet quality, and other social factors are crucial contributors to these outcomes.
In conclusion, this analysis demonstrates that the relationship between nutrition and life expectancy is multifaceted. While caloric and protein intake are important, they must be understood within the broader context of healthcare, lifestyle, and socio-economic conditions.
References
Literature
Cao, G., Liu, J., Liu, M., & Liang, W. (2023). Effects of the COVID-19 pandemic on life expectancy at birth at the global, regional, and national levels: A joinpoint time-series analysis. Journal of Global Health, 13. https://doi.org/10.7189/jogh.13.06042.
Ekmekcioglu, C. (2020). Nutrition and longevity: From mechanisms to uncertainties. Critical Reviews in Food Science and Nutrition, 60(19), 3063–3082. https://doi.org/10.1080/10408398.2019.1676698
Godfray, H. C. J., Aveyard, P., Garnett, T., Hall, J. W., Key, T. J., Lorimer, J., Pierrehumbert, R. T., Scarborough, P., Springmann, M., & Jebb, S. A. (2018). Meat consumption, health, and the environment. Science, 361(6399), eaam5324. https://doi.org/10.1126/science.aam5324
Katz, D. L., & Meller, S. (2014). Can we say what diet is best for health? Annual Review of Public Health, 35, 83–103. https://doi.org/10.1146/annurev-publhealth-032013-182351
Li, Y., Pan, A., Wang, D., Liu, X., Dhana, K., Franco, O., Kaptoge, S., Angelantonio, E., Stampfer, M., Willett, W., & Hu, F. (2018). Impact of healthy lifestyle factors on life expectancies in the US population. Circulation, 138(4), 345–355. https://doi.org/10.1161/CIRCULATIONAHA.117.032047
Lieberman, H. R., Fulgoni, V. L., Agarwal, S., Pasiakos, S. M., & Berryman, C. E. (2020). Protein intake is more stable than carbohydrate or fat intake across various US demographic groups and international populations. The American Journal of Clinical Nutrition, 112(1), 180–186. https://doi.org/10.1093/ajcn/nqaa044
van Vliet, S., Burd, N. A., & van Loon, L. J. C. (2015). The skeletal muscle anabolic response to plant- versus animal-based protein consumption. The Journal of Nutrition, 145(9), 1981–1991. https://doi.org/10.3945/jn.114.204305
Datasets
FAOSTAT. (2022). Food Balance Sheets (FBS): Macronutrient consumption per capita. Food and Agriculture Organization of the United Nations. Retrieved from https://www.fao.org/faostat/en/#data/FBS
World Health Organization. (2022). Life expectancy at birth (years). Global Health Observatory (GHO) data. Retrieved from https://www.who.int/data/gho/data/indicators/indicator-details/GHO/life-expectancy-at-birth-(years)
GitHub Gist. (n.d.). Country Capital Lat/Lon list. Retrieved from https://gist.github.com/ofou/df09a68348a421b4f376c875194915c9#file-country-capital-lat-long-population-csv